Analytic Apps and the Need for a Resilient Infrastructure
Brittany Gotschall 2700050P02 email@example.com | 2013-05-08 11:55:09.0 | Tags:  analytics idc business-analytics | 0 Comments | 1,612 Visits
By Jean Bozman, Research Vice President, Enterprise Servers, and Dan Vesset, Program Vice President, Business Analytics, IDC
To read part 1 of this two-part series click here.
In a recent IDC survey of over 2,500 organizations, 22% of respondents indicated that if their business analytics solution were out of service for up to one hour, it would have a material negative impact on business operations. That's why IT groups are paying even more attention to resiliency around analytics deployments.
Resiliency is defined by two key attributes: availability and flexibility. For production systems, enterprise workloads must be highly available because they may support hundreds, or even thousands, of end users accessing them. Although some analytic applications may be used by small groups of data scientists and business unit analysts, availability is still important to overall business success as the role of these analysts is increasingly critical in providing insight to other decision makers in the organization.
Building a more agile infrastructure gives IT organizations the flexibility to better support analytics workloads. Having IT flexibility means that organizations don't need to rip and replace infrastructure. Instead, they are more likely to add net-new servers to existing infrastructure or to reorganize servers into clusters, grids, or arrays that run analytics software. Read this IDC paper for a fuller discussion on matching analytics workloads with infrastructure and client deployments examples.)
Manufacturing, healthcare, telecommunications, public sector, and other organizations that have higher competency and pervasiveness of business analytics solutions are defined by their focus not only on software functionality for information integration, monitoring, management, analysis, and visualization, but also on innovating the hardware infrastructure that enables successful business analytics projects and ongoing programs.
These organizations place a premium on the optimization and resiliency of server, storage, and network infrastructure to address expanding volumes of multi-structured data, users, and use cases.